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Collaborative tasks often begin with partial task knowledge and incompleteinitial plans from each partner. To complete these tasks, agents need to engagein situated communication with their partners and coordinate their partialplans towards a complete plan to achieve a joint task goal. While suchcollaboration seems effortless in a human-human team, it is highly challengingfor human-AI collaboration. To address this limitation, this paper takes a steptowards collaborative plan acquisition, where humans and agents strive to learnand communicate with each other to acquire a complete plan for joint tasks.Specifically, we formulate a novel problem for agents to predict the missingtask knowledge for themselves and for their partners based on rich perceptualand dialogue history. We extend a situated dialogue benchmark for symmetriccollaborative tasks in a 3D blocks world and investigate computationalstrategies for plan acquisition. Our empirical results suggest that predictingthe partner's missing knowledge is a more viable approach than predicting one'sown. We show that explicit modeling of the partner's dialogue moves and mentalstates produces improved and more stable results than without. These resultsprovide insight for future AI agents that can predict what knowledge theirpartner is missing and, therefore, can proactively communicate such informationto help their partner acquire such missing knowledge toward a commonunderstanding of joint tasks.

Towards Collaborative Plan Acquisition through Theory of Mind Modeling in Situated Dialogue